In contrast to the standard Transformer [1]},
the coefficients in slot attention are normalized over all slots.
This choice of normalization introduces competition between the slots to explain parts of the input,
and ensures each pixel is assigned to a query vector:
\(&\text{attn}_{i,j} := \frac{e^{M_{i,j}}}{\sum _l e^{M_{i,l}}} \text{\hspace{5.0pt}} \\ \nonumber &M := \frac{1}{\sqrt{D}}k^T \cdot q,\text{\hspace{5.0pt}attn} \in \mathcal {R}^{HW \times 2}\) 
